import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error


class simple_lstm:
    def __init__(self, point_file, batch_size, epochs, look_back):
        self.look_back = look_back
        self.batch_size = batch_size
        self.epochs = epochs
        self.point_file = point_file
        self.compile_model()

    def compile_model(self):
        self.model = Sequential()
        self.model.add(LSTM(128, input_shape=(1, self.look_back)))
        self.model.add(Dense(1))
        self.model.compile(loss='mean_squared_error', optimizer='adam')

    def train(self):
        # convert an array of values into a dataset matrix

        def create_dataset(dataset, look_back=1):
            dataX, dataY = [], []
            for i in range(len(dataset)-look_back-1):
                a = dataset[i:(i+look_back), 0]
                dataX.append(a)
                dataY.append(dataset[i + look_back, 0])
            return numpy.array(dataX), numpy.array(dataY)

        # fix random seed for reproducibility
        numpy.random.seed(7)
        # load the dataset
        dataframe = read_csv(self.point_file, usecols=[
                             1], engine='python', skipfooter=3)
        dataset = dataframe.values
        dataset = dataset.astype('float32')
        # normalize the dataset
        scaler = MinMaxScaler(feature_range=(0, 1))
        dataset = scaler.fit_transform(dataset)
        # split into train and test sets
        train_size = int(len(dataset) * 0.67)
        test_size = len(dataset) - train_size
        train, test = dataset[0:train_size,
                              :], dataset[train_size:len(dataset), :]
        # reshape into X=t and Y=t+1
        trainX, trainY = create_dataset(train, self.look_back)
        testX, testY = create_dataset(test, self.look_back)
        # reshape input to be [samples, time steps, features]
        trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
        testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
        # create and fit the LSTM network
        self.model.fit(trainX, trainY, epochs=self.epochs,
                       batch_size=self.batch_size, verbose=0)

        # make predictions
        trainPredict = self.model.predict(trainX)
        testPredict = self.model.predict(testX)
        # invert predictions
        trainPredict = scaler.inverse_transform(trainPredict)
        trainY = scaler.inverse_transform([trainY])
        testPredict = scaler.inverse_transform(testPredict)
        testY = scaler.inverse_transform([testY])
        # calculate root mean squared error
        trainScore = math.sqrt(mean_squared_error(
            trainY[0], trainPredict[:, 0]))
        print('Train Score: %.2f RMSE' % (trainScore))
        testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:, 0]))
        print('Test Score: %.2f RMSE' % (testScore))
        # shift train predictions for plotting
        # trainPredictPlot = numpy.empty_like(dataset)
        # trainPredictPlot[:, :] = numpy.nan
        # trainPredictPlot[self.look_back:len(trainPredict)+self.look_back, :] = trainPredict
        # # shift test predictions for plotting
        # testPredictPlot = numpy.empty_like(dataset)
        # testPredictPlot[:, :] = numpy.nan
        # testPredictPlot[len(trainPredict)+(self.look_back*2)+1:len(dataset)-1, :] = testPredict
        # # plot baseline and predictions
        # plt.plot(scaler.inverse_transform(dataset))
        # plt.plot(trainPredictPlot)
        # plt.plot(testPredictPlot)
        # plt.show()

    def run(self):
        self.train()
